| hbinom | R Documentation |
Probability mass function, distribution function, and random generation for the hurdle (zero-altered) binomial distribution.
dhbinom(x, size, prob, zeroprob = 0.5, log = FALSE)
phbinom(q, size, prob, zeroprob = 0.5, lower.tail = TRUE, log.p = FALSE)
rhbinom(n, size, prob, zeroprob = 0.5)
x, q |
integer vector of counts |
size |
number of trials (zero or more) |
prob |
probability of success on each trial |
zeroprob |
probability of a zero, between 0 and 1 |
log, log.p |
logical; return log-density if TRUE |
lower.tail |
logical; if |
n |
number of random values to return. |
This implementation allows for automatic differentiation with RTMB.
A hurdle distribution models the zeros and the positive counts as two separate
processes: the probability of a zero is a free parameter, and the positive
counts follow the corresponding zero-truncated distribution. Writing
p_0 for zeroprob,
P(X = 0) = p_0, \qquad
P(X = x) = (1 - p_0)\,\frac{P_{\mathrm{Bin}}(x;\,n,\pi)}{1 - \pi_0},
\quad x = 1, \ldots, n.
where \pi_0 = P_{\mathrm{Bin}}(0;\,n,\pi) is the probability of a zero under
the ordinary binomial.
Unlike zero-inflation, which can only add zeros to those the binomial already
produces, zeroprob here is exactly the probability of a zero and may be
larger or smaller than \pi_0. The two coincide with the
ordinary binomial when zeroprob equals \pi_0.
dhbinom gives the probability mass function, phbinom gives the distribution function, and rhbinom generates random deviates.
Mullahy, J. (1986) Specification and testing of some modified count data models. Journal of Econometrics, 33, 341-365.
Rigby, R. A., Stasinopoulos, D. M., Heller, G. Z., and De Bastiani, F. (2019) Distributions for modeling location, scale, and shape: Using GAMLSS in R, Chapman and Hall/CRC, doi:10.1201/9780429298547. An older version can be found in https://www.gamlss.com/.
hpois, hnbinom, zibinom, ztbinom
set.seed(123)
x <- rhbinom(5, size = 10, prob = 0.3, zeroprob = 0.4)
d <- dhbinom(x, size = 10, prob = 0.3, zeroprob = 0.4)
p <- phbinom(x, size = 10, prob = 0.3, zeroprob = 0.4)
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